Proceedings of the Annual Meeting of the Cognitive Science Society

Distributed representations, in which information is encodedin overlapping populations of neuronal units, are essential tothe remarkable success of artificial neural networks (ANNs) inmany domains, and have been posited to be employed through-out the brain, especially in neocortex. A fundamental signatureof ANNs employing distributed representations is that learningrequires exposure to information in an interleaved order; expo-sure to new information in a blocked order tends to overwriteprior knowledge (i.e., ’catastrophic interference’). Because itis difficult to match human learning to the learning conditionsof these networks, it is not known whether human learning ex-hibits these properties, which, if true, would implicate use ofsimilar representations. To test this, we leveraged a recent pro-posal that parts of the hippocampus host distributed represen-tations of the kind typically ascribed to neocortex, and adopteda hippocampally dependent task that contrasts the effects of in-terleaved versus blocked learning on a short timescale. Exper-iments 1a and 1b demonstrate that interleaved exposure facili-tates the rapid perception of shared structure across items. Ex-periment 2 shows that only interleaved exposure permits use-ful inference when item associations need to be inferred basedon statistical regularities. Together, these results demonstratethe power of interleaved learning and implicate the use of dis-tributed representations in human rapid learning of structuredinformation.